Short-Term Exposure to Fine Particulate Matter and Nitrogen Dioxide and Mortality in 4 Countries

Key Points Question What are the associations between short-term changes in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations and changes in daily all-cause mortality rates? Findings This cross-sectional study of more than 8.9 million deaths found that a 10-μg/m3 increase in daily PM2.5 concentrations was associated with increases in daily all-cause deaths per 100 000 people of 0.01 in Jiangsu, China, 0.03 in California, 0.10 in central-southern Italy, and 0.04 in Germany; corresponding increases in mortality rates for the same increase in NO2 concentrations were 0.04, 0.03, 0.10, and 0.05, respectively. Meaning The findings of this study add to the growing evidence that increases in short-term exposures to PM2.5 and NO2 may be associated with increases in mortality rates.

eAppendix 2. Air Pollution Spatiotemporal Models for Jiangsu, China, Central-Southern Italy, and Germany For Jiangsu, China, we obtained the spatiotemporal models at 1 × 1 km 2 resolution for NO2 and PM2.5, 2015-2019, that were developed by Dr. Meng Wang's team from University at Buffalo. 1,2Briefly, the models incorporated variables from satellite, chemical transport model, land cover, elevation and slope, road network, urban density, and meteorological data to predict daily concentrations of air pollution in China, and utilized machine-learning algorithms, including random forests, extremely randomized trees, and extreme gradient boosting.Across the country, the overall cross-validation R 2 was 0.72 and 0.88 for NO2 and PM2.5, respectively.Stafoggia et al. 3,4 estimated the daily PM2.5 and NO2 concentrations, in addition to concentrations of other pollutants, for 2015-2019 at 1 × 1 km 2 resolution using a random forest machine learning approach in Italy.For PM2.5 only, a stage 1 was carried out to expand the number of stations and measuring points, based on the collocated PM10.Thereafter, separately for each year and for each pollutant, a random forest model was trained with daily concentrations of the pollutant as the response variable and spatial and temporal variables such as climate zone, land cover, population, elevation, aerosol optical depth, daily mean air temperature, sea-level barometric pressure, precipitations, relative humidity, wind speed and direction, planetary boundary layer height, normalized difference vegetation index, and desert dust advection days, as predictors.
We obtained data for NO2 and PM2.5 in Germany from the German Environment Agency at a spatial resolution of 2 x 2 km 2 for 2015-2019.The data are based on a data assimilation technique known as Optimal Interpolation, 5 which uses the chemical REM-CALGRID model and integrates measured air pollutant concentrations from background monitoring stations.The final results are nationwide air quality maps based on information both of the observed and the modeled fields. 6To adapt the data to the requirements of the INSPIRE Directive (a European Union spatial data infrastructure) and to make it more comparable to the other regions in this project, the data were downscaled to a resolution of 1x1 km 2 by bilinear interpolation.

eAppendix 3. Relative Humidity Calculation in Each Region
For Jiangsu, China; California, U.S.; and Central-southern Italy, dew point temperature data at 0.1° × 0.1° resolution were extracted from the ERA5-Land reanalysis dataset 7 and were averaged to daily spatial-unit level.In these three regions, daily relative humidity (RH) was calculated based on the information on air temperature and dew point temperature by the following steps.
Step 1. Calculate the vapor pressure () The vapor pressure  was calculated from a given dew point   (in K) by using the Clausius-Clapeyron relation: where   ( 0 ) = 6.11hPa is the saturation vapor pressure at a reference temperature  0 = 273.Step 2. Calculate the saturation vapor pressure (  ) The saturation vapor pressure   was calculated from a given air temperature  (in K) by the same equation: Step 3. Calculate the relative humidity () Finally, we calculated the relative humidity  using the vapor pressure and saturation vapor pressure values: The calculation of RH in these three regions was performed with R package humidity. 9r Germany, daily mean RH at a spatial resolution of 1 x 1 km 2 was derived from spatiotemporal models for the years 2015-2019.In brief, the German-wide RH was predicted by applying a random forest model which incorporated data from multiple sources such as ground-based RH observations, modeled air temperature, wind speed, and precipitation as well as remote sensing elevation, greenness, and the visible light bands (red, green and blue). 10The date information was also added to represent the temporal variability of the relationship between the response and predictor variables.The 10-fold cross-validation results of the random forest model showed high performance (R 2 = 0.80 and Root Mean Square Error (RMSE) of 5.42 %) and they were externally confirmed by a comparison against an independent and dense monitoring network in the Augsburg region (R 2 ≥ 0.84, RMSE ≤ 5.91 %).The number of common time-varying factors  , in the interactive fixed effects model was selected based on the criteria proposed by Bai and Ng (2002). 11The number of factors  ̂ can be obtained by minimizing the following criterion: for all  ∈ {1,2, … }, where  is the number of spatial units,  is the number of days, and  ̂, () is the fitted value for a given factor dimension . , is a penalty term, which penalizes the undesired variance reduction caused by an increasing number of factors  ̂.This penalty term can be estimated by where  ̂2 is the sample variance estimator of the residuals  , .This variance estimator  ̂2 can be obtained by where   is an arbitrary maximal dimension of factors that is greater than .In our study, we set   as the square root of the number of spatial units in each study region.All statistical analyses were conducted with R software (version 4.1.3)using the package phtt.We compared the results from the IFE model with those from a traditional two-stage time-series model, with model settings similar to those in previous studies. 14,15 the first stage, we estimated the association of PM2.5 or NO2 with mortality for each county or municipality using a quasi-Poisson generalized linear model, controlling for seasonality and long-term day of the week, and air temperature: where E(Mortality count) is the daily morality count in each spatial unit; µ is the intercept; Air pollution is the daily PM2.5 or NO2 concentration in each spatial unit; DOW is an indicator of the day of the week; the seasonality and long-term trend were controlled by a natural cubic spline over the range of study dates with 6 degrees of freedom (df) per year; and air temperature on the same lag as air pollution was controlled by a natural cubic spline with 5 dfs.
In the second stage, a univariate random-effect meta-analysis was used to pool the county/ municipalityspecific risk estimates into a single summary estimate of association in each study region.As in the main IFE model, we explored the lag pattern on the current day and the previous two days using both single lag days (lag0 to lag2) and cumulative lag days (lag01 to lag02), and the main lag was defined as the lag with the greatest effect size.
© 2024 Ma Y et al.JAMA Network Open.This figure shows the distribution of the model estimates for main lags when the PM2.5 or NO2 concentration was randomized 2,000 times across days in each spatial unit.The distributions of the estimates from the randomization tests (the histograms) were approximately centered at zero and the coefficient estimates from our single-pollutant models (the orange lines) for the main lag for all study regions fell substantially outside these distributions, indicating that the estimated associations between changes in air pollution and changes in mortality rate in these regions were unlikely driven by temporal dependence due to a model misspecification.). represents the estimated number of common time-varying factors in the unmeasured time-varying county effect (this spatial unit effect was estimated to be 0 in Central-southern Italy and Germany). was selected following the criteria proposed by Bai and Ng (2002) (see details in eAppendix 4).In the plots of the decomposed common time-varying factors, each color represents each decomposed common time-varying factor; in the plots of the unmeasured time-varying county effects, each color represents each county.

eFigure 3. Association Between Air Pollution and Mortality Estimated From Interactive Fixed-Effects Model and Traditional 2-Stage Time-Series Analysis
This plot compares the results from the main interactive fixed effects model and the comparative traditional two-stage time-series analysis on the main lag (the lag with the greatest effect size).The estimates from the two models have different interpretations: the estimates from the IFE model represent the estimated change in daily mortality rate (per 100,000 people) associated with a 10 µg/m 3 increase in PM2.5 or NO2 concentrations, while the estimates from the time-series analysis represent the percent change in mortality for the same increase in PM2.5 or NO2.The results from both models suggested that short-term exposure to PM2.5 and NO2 was associated with an increase in all-cause mortality.

eFigure 1 .
Randomization Test for the Main Lag in Each Study Region

eTable 1 .
Estimated Change in Daily Mortality Rate (per 100 000 People) Associated With a 10µg/m 3 Increase in PM2.5 or NO2 Concentration (95% CI) by Lag Estimated Changes in Daily Mortality Rate (per 100 000 People) Associated With a 10-µg/m 3 Increase in PM2.5 and NO2 Concentrations in Sensitivity Analyses a aIn stratified analyses, we used the lag day with the greatest pollutant-mortality association for each pollutant in each region (Jiangsu, Chinab We tested the statistical differences in effect estimates between urban and rural areas by calculating P values based on the z score derived from the coefficients and standard errors for urban vs. rural.©2024 Ma Y et al.JAMA Network Open.eTable 3.